Ruben Hemelings – Ignite Speaker at DIS 2017

Ruben Hemelings is a graduate student in Artificial Intelligence / Big Data at the Catholic University of Leuven.dis17_speaker_rubin-h In collaboration with the Flemish research organisation VITO, he is currently researching the application of deep learning on retinal images in order to improve early diagnosis of ocular and cardiovascular diseases. Ruben also holds a business engineering degree from the Solvay Brussels School of Economics and Management, and went on an exchange program in Canada. Prior to his interest in data, he considered a career in finance, illustrated by summer internships at ING, Deloitte and PwC. In his spare time, you can often find him playing the piano or having a go at tackling Kaggle challenges.

Join his Ignite speech on “Deep learning on biomedical images” at diSummit 2017 !

Next Executive Session: What about a debate on Big Data & Ethics ?

Pierre Nicolas Schwab -EBU_Big_Data

 

Next Executive Sessions :

After the interesting talk of Stephen Brobst CTO#4 we now want to organise a debate on Big Data & Ethics. Pierre-Nicolas is proposing the following format: Short presentations by each participant representing the vision of his/her company followed by a debate. Any suggestions ? Who wants to participate ?

 

Philippe, Puis-je te proposer que tu organises un atelier de travail avec une dizaine de responsables Big Data sur l’éthique et le Big Data ? 
Il faudrait limiter l’audience à des décisionnaires Big Data qui puissent vraiment représenter le point de vue de leur société.
En termes de structuration :
 – 10-15 min (TED format) de présentation de chaque société sur les enjeux éthiques, sur la manière dont les algorithms sont conçus pour tenir compte de ces aspects éthiques, sur les enjeux et limites du Big Data, ce que les entreprises s’interdisent de faire en termes de collecte de données (la “ligne rouge” à ne pas dépasser)
 – un atelier d’échanges / débat

 


Here is some inspiration on the topic.

The RTBF’s big data expert Pierre Nicolas Schwab reflects on how to develop algorithms for PSM which match their values and mitigate the potential flaws of artificial intelligence. He invites EBU Big Data Initiative participants to join the RTBF workshop addressing this issue this December in Brussels.

“Most large companies are investing massively in Big Data technologies to leverage the value of their data. While many still consider Big Data as an inescapable business trend, concerns are growing regarding the impact of Big Data on our daily lives.

A ‘man vs machine’ Artificial Intelligence (AI) milestone was reached in March this year when the deep-learning algorithm AlphaGo defeated one of the world’s best players at Go, Lee Sedol. In an article I published before the game, I was wondering how advances in AI were changing our lives. Cathy O’Neil, a Harvard PhD mathematician, expressed similar concerns at the USI conference in Paris in early June and will be releasing her book “Weapons of Math Destructions” in September. This book elaborates on the concerns she has expressed on her blog about ill-based decisions triggered by algorithms and how big data “increases inequality and threatens democracy”. The title may be provocative but promises to go beyond the filter bubble effect made popular by Eli Pariser.

Because algorithms are only as good as those who build them, we need to open up the models, and not only the data. Those models need to be subject to criticism, peer-review and third-party scrutiny. This will avoid the use of biased or even dangerous algorithms (e.g. the French universities selection algorithm scandal revealed earlier this month) and will increase people’s trust in organizations which use algorithms. To illustrate the latter, the French fiscal authorities are now forced to render public the criteria that play a role when submitting a tax payer to a control. This exemplifies that a change is ongoing and, as PSM, we must embrace and support it.

Not only must we avoid replicating flawed models (in particular recommendation algorithms that trap users in “filter bubbles”) but we, as public organizations, have duties towards our democratic societies and their citizens. That’s why we need to (1) engage in a global reflection on how our algorithms need to be shaped to reflect our values and (2) pave the way for better practices that will inspire other organizations in different industries.

 

Job – Junior Data Scientist

Screenshot 2016-07-01 12.02.02

Are you pursuing a career in data science?

We have a great opportunity for you: an intensive training program combined with interesting job opportunities!

Interested? Check out http://di-academy.com/bootcamp/ follow the link to our datascience survey and send your cv to training@di-academy.com

Once selected, you’ll be invited for the intake event that will take place in Brussels this summer.

Hope to see you there,

Nele & Philippe

The ABC of Datascience blogs – collaborative update

abc-letters-on-white-sandra-cunningham

A – ACID – Atomicity, Consistency, Isolation and Durability

B – Big Data – Volume, Velocity, Variety

C – Columnar (or Column-Oriented) Database

  • CoolData By Kevin MacDonell on Analytics, predictive modeling and related cool data stuff for fund-raising in higher education.
  • Cloud of data blog By Paul Miller, aims to help clients understand the implications of taking data and more to the Cloud.
  • Calculated Risk, Finance and Economics

D – Data Warehousing – Relevant and very useful

E – ETL – Extract, transform and load

F – Flume – A framework for populating Hadoop with data

  • Facebook Data Science Blog, the official blog of interesting insights presented by Facebook data scientists.
  • FiveThirtyEight, by Nate Silver and his team, gives a statistical view of everything from politics to science to sports with the help of graphs and pie charts.
  • Freakonometrics Charpentier, a professor of mathematics, offers a nice mix of generally accessible and more challenging posts on statistics related subjects, all with a good sense of humor.
  • Freakonomics blog, by Steven Levitt and Stephen J. Dubner.
  • FastML, covering practical applications of machine learning and data science.
  • FlowingData, the visualization and statistics site of Nathan Yau.

G – Geospatial Analysis – A picture worth 1,000 words or more

H – Hadoop, HDFS, HBASE

  • Harvard Data Science, thoughts on Statistical Computing and Visualization.
  • Hyndsight by Rob Hyndman, on fore­cast­ing, data visu­al­iza­tion and func­tional data.

I – In-Memory Database – A new definition of superfast access

  • IBM Big Data Hub Blogs, blogs from IBM thought leaders.
  • Insight Data Science Blog on latest trends and topics in data science by Alumnus of Insight Data Science Fellows Program.
  • Information is Beautiful, by Independent data journalist and information designer David McCandless who is also the author of his book ‘Information is Beautiful’.
  • Information Aesthetics designed and maintained by Andrew Vande Moere, an Associate Professor at KU Leuven university, Belgium. It explores the symbiotic relationship between creative design and the field of information visualization.
  • Inductio ex Machina by Mark Reid’s research blog on machine learning & statistics.

J – Java – Hadoop gave it a nice push

  • Jonathan Manton’s blog by Jonathan Manton, Tutorial-style articles in the general areas of mathematics, electrical engineering and neuroscience.
  • JT on EDM, James Taylor on Everything Decision Management
  • Justin Domke blog, on machine learning and computer vision, particularly probabilistic graphical models.
  • Juice Analytics on analytics and visualization.

K – Kafka – High-throughput, distributed messaging system originally developed at LinkedIn

L – Latency – Low Latency and High Latency

  • Love Stats Blog By Annie, a market research methodologist who blogs about sampling, surveys, statistics, charts, and more
  • Learning Lover on programming, algorithms with some flashcards for learning.
  • Large Scale ML & other Animals, by Danny Bickson, started the GraphLab, an award winning large scale open source project

M – Map/Reduce – MapReduce

N – NoSQL Databases – No SQL Database or Not Only SQL

O – Oozie – Open-source workflow engine managing Hadoop job processing

  • Occam’s Razor by Avinash Kaushik, examining web analytics and Digital Marketing.
  • OpenGardens, Data Science for Internet of Things (IoT), by Ajit Jaokar.
  • O’reilly Radar O’Reilly Radar, a wide range of research topics and books.
  • Oracle Data Mining Blog, Everything about Oracle Data Mining – News, Technical Information, Opinions, Tips & Tricks. All in One Place.
  • Observational Epidemiology A college professor and a statistical consultant offer their comments, observations and thoughts on applied statistics, higher education and epidemiology.
  • Overcoming bias By Robin Hanson and Eliezer Yudkowsky. Present Statistical analysis in reflections on honesty, signaling, disagreement, forecasting and the far future.

P – Pig – Platform for analyzing huge data sets

  • Probability & Statistics Blog By Matt Asher, statistics grad student at the University of Toronto. Check out Asher’s Statistics Manifesto.
  • Perpetual Enigma by Prateek Joshi, a computer vision enthusiast writes question-style compelling story reads on machine learning.
  • PracticalLearning by Diego Marinho de Oliveira on Machine Learning, Data Science and Big Data.
  • Predictive Analytics World blog, by Eric Siegel, founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times, makes the how and why of predictive analytics understandable and captivating.

Q – Quantitative Data Analysis

R – Relational Database – Still relevant and will be for some time

  • R-bloggers , best blogs from the rich community of R, with code, examples, and visualizations
  • R chart A blog about the R language written by a web application/database developer.
  • R Statistics By Tal Galili, a PhD student in Statistics at the Tel Aviv University who also works as a teaching assistant for several statistics courses in the university.
  • Revolution Analytics hosted, and maintained by Revolution Analytics.
  • Rick Sherman: The Data Doghouse on business and technology of performance management, business intelligence and datawarehousing.
  • Random Ponderings by Yisong Yue, on artificial intelligence, machine learning & statistics.

S – Sharding (Database Partitioning)  and Sqoop (SQL Database to Hadoop)

  • Salford Systems Data Mining and Predictive Analytics Blog, by Dan Steinberg.
  • Sabermetric Research By Phil Burnbaum blogs about statistics in baseball, the stock market, sports predictors and a variety of subjects.
  • Statisfaction A blog by jointly written by PhD students and post-docs from Paris (Université Paris-Dauphine, CREST). Mainly tips and tricks useful in everyday jobs, links to various interesting pages, articles, seminars, etc.
  • Statistically Funny True to its name, epidemiologist Hilda Bastian’s blog is a hilarious account of the science of unbiased health research with the added bonus of cartoons.
  • SAS Analysis, a weekly technical blog about data analysis in SAS.
  • SAS blog on text mining on text mining, voice mining and unstructured data by SAS experts.
  • SAS Programming for Data Mining Applications, by LX, Senior Statistician in Hartford, CT.
  • Shape of Data, presents an intuitive introduction to data analysis algorithms from the perspective of geometry, by Jesse Johnson.
  • Simply Statistics By three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry) who are fired up about the new era where data are abundant and statisticians are scientists.
  • Smart Data Collective, an aggregation of blogs from many interesting data science people
  • Statistical Modeling, Causal Inference, and Social Science by Andrew Gelman
  • Stats with Cats By Charlie Kufs has been crunching numbers for over thirty years, first as a hydrogeologist and since the 1990s, as a statistician. His tagline is- when you can’t solve life’s problems with statistics alone.
  • StatsBlog, a blog aggregator focused on statistics-related content, and syndicates posts from contributing blogs via RSS feeds.
  • Steve Miller BI blog, at Information management.

T – Text Analysis – Larger the information, more needed analysis

U – Unstructured Data – Growing faster than speed of thoughts

V – Visualization – Important to keep the information relevant

  • Vincent Granville blog. Vincent, the founder of AnalyticBridge and Data Science Central, regularly posts interesting topics on Data Science and Data Mining

W – Whirr – Big Data Cloud Services i.e. Hadoop distributions by cloud vendors

X – XML – Still eXtensible and no Introduction needed

  • Xi’an’s Og Blog A blog written by a professor of Statistics at Université Paris Dauphine, mainly centred on computational and Bayesian topics.

Y – Yottabyte – Equal to 1,000 exabytes, 1 million petabytes and 1 billion terabytes

Z – Zookeeper – Help managing Hadoop nodes across a distributed network

Feel free to add your preferred blog in the comment bellow.

Other resources:

Nice video channels:

More Jobs ?

hidden-jobs1

Click here for more Data related job offers.
Join our community on linkedin and attend our meetups.
Follow our twitter account: @datajobsbe

Improve your skills:

Why don’t you join one of our  #datascience trainings in order to sharpen your skills.

Special rates apply if you are a job seeker.

Here are some training highlights for the coming months:

Check out the full agenda here.

Join the experts at our Meetups:

Each month we organize a Meetup in Brussels focused on a specific DataScience topic.

Brussels Data Science Meetup

Brussels, BE
1,417 Business & Data Science pro’s

The Brussels Data Science Community:Mission:  Our mission is to educate, inspire and empower scholars and professionals to apply data sciences to address humanity’s grand cha…

Next Meetup

DATA UNIFICATION IN CORPORATE ENVIRONMENTS

Wednesday, Oct 14, 2015, 6:30 PM
57 Attending

Check out this Meetup Group →

New Survey and (Big) Data Governance Research by Andra Mertilos

data-governance

Dear friends from the Brussels Data Science community, I would like to ask for your help in conducting my master thesis research (aside from my consultancy job, I am also an MBA student at the KU Leuven) which I launched recently.

Data governance research is ambiguous in the scientific community today, mostly due to the differences existing between the concepts which form the building blocks of a governance program : data and information, governance and management, IT and business labels,…As such, there exists no homogeneous definition nor in the scientific community, nor in the practitioner one as to what data governance really encompasses. Correctly positioning data governance in todays landscape will allow for the integration of new technologies, concepts and phenomena. But for this positioning to take place, specifying a common data governance definition proves to be crucial in determining and isolating the different elements which constitute the backbone of such programs. Identifying, defining and explaining the process layers, responsibilities and decision-making structures that come together and interact in governance topics allows for prioritizing and ranking the elements which constitute a data governance program. These layers allow in return for tailoring to specific needs and requirements such as integration of new concepts and phenomena like big data technologies.

The purpose of my research is specifically to build a (big) data governance maturity model which holds for the Belgian financial sector. I choose the banking sector because of its size and complexity: such an intricate environment allows for building a larger, more comprehensive data governance model which could further be tailored to fit to other sectors.

For this purpose, based on an extensive literature review, I have built a questionnaire, in the form of a maturity assessment, which evaluates different data governance aspects : MDM, enterprise architecture, technology, applications, big data initiatives etc.

I would like to draw on your input, experience and expertise for the evaluation of this model in practice by inviting you to participate in my survey. A possible prerequisite would be to have some former experience in the banking/financial sector (in any form pertaining to data programs) but as such, it is not an exclusion criteria, as I plan to use these answers to develop a standard capability maturity model for data governance. If you choose to participate please bear in mind that you don’t have to be “able” to answer all sections – the questionnaire dealing with multiple aspects of governance – your feedback on a singular issue being also of extreme value to this research.

The link to survey can be found here: https://qtrial2014az1.az1.qualtrics.com/jfe/form/SV_1B4wOzh3GVKTnoh

It takes an average of 10-15 minutes to complete the survey and the link will be available until begin of April.

Feel free to contact me should you have any questions or remarks (at mertilosandra@gmail.com), any feedback is great feedback 🙂
Many thanks in advance for your participation.

About Andra Mertilos :

Andra Mertilos

Andra Mertilos

mertilosandra@gmail

Thank you for your help in conducting my master thesis research

I am a MBA student at the KU Leuven

The link to survey can be found here: https://qtrial2014az1.az1.qualtrics.com/jfe/form/SV_1B4wOzh3GVKTnoh

Startup Launch – Predicube – MORE EFFECTIVE ONLINE ADVERTIZING

logo

 

 

  • Stop sending all your money to facebook and google, spend it more wisely and get far better results from a Belgian #datascience startup offering a more secure and safer solution for Online advertising.
  • Invest in more effective targeted online advertising while respecting consumers’ privacy concerns

 

 David Martens  crowdPhilippe Degueldre Prof. Foster Provost    Book-cover

Nice crowd yesterday for the on  the top floor of the  Boerentoren (KBCstartit) for the Launch of Predicube, a startup founded by David Martens.

We had 3 presentations:

  • Welcome and introduction by David Martens
  • Philippe Degueldre, director business intelligence at Pebble Media
  • Prof. Foster Provost, Professor at NYU’s SternBusiness School and Co-founder of several big data companies

70% of every euro that is redirected from print to online advertising currently floats out of the local economy

 

Online advertising is big business, but spamming consumers with random ads has proven not to be the best way to optimize advertising returns: people only click on ads that are relevant to them. Hence, media companies and advertisers have been exploring targeted advertising strategies to boost online ads’ click-through rates (and revenues) based on an analysis of people’s Internet surfing patterns. An approach that conflicts with consumers’ stringent privacy concerns?

Not anymore, thanks to Belgian tech starter PrediCube – a spin-off from digital research center iMinds and University of Antwerp, and supported by the Start it @KBC incubator. PrediCube uses big data analytics to make sure consumers get to see those ads that are truly of interest to them, thereby increasing click-through rates up to 300%, while putting its unique ‘privacy by design’ strategy center stage.

Analyzing and predicting consumers’ online behavior to increase click-through rates up to 300%

Online adverteren is big business. Online advertising is big business. In Europe alone, online ad spending topped €27 Billion in 2013 (a YoY increase of 11,9%). Yet, while this big market potential offers a great deal of opportunities, the shift to online advertising also comes with a number of important concerns. The authors of the book Het nieuwe TV-kijken1 found, for instance, that 70% of every euro that is redirected from print to online advertising currently floats out of the local economy – right into the hands of a few big international players such as Google and Facebook.

Trying to counter that drain of resources and valuable consumer data, PrediCube now brings to market a solution that can be used by local media advertising companies to predict consumers’ interest in specific ads – based on an analysis of their online behavior. Result: targeted online advertising campaigns that are much more efficient and generate higher revenues.

“Using PrediCube, we have been able to increase targeted online ads’ click-through rates up to 300%. In other words, we are now able to match ads with the right consumer profiles up to 3 times more accurately. Moreover, we want to significantly increase the inventory’s volume based on socio-demographic criteria (such as age and gender). Thanks to PrediCube we will be able to do this in the very near future. It goes without saying that this approach will positively impact our business and product offering,” says Philippe Degueldre, director business intelligence at Pebble Media, managing online advertising for 80 premium websites – such as VRT, Telenet, RTBF, Viacom, Elle and LinkedIn.

Strong focus on ‘privacy by design’

Dealing adequately with consumers’ privacy concerns is a major focus area for the PrediCube team; hence they are investing a great deal of effort in their ‘privacy by design’ approach.

“PrediCube works by means of cookies,” explains Prof. dr. David Martens, co-founder of PrediCube and Assistant Professor at the Faculty of Applied Economics, University of Antwerp. “First of all, web pages that use the PrediCube customer behavior prediction tool will ask users’ explicit consent to use those cookies. If the cookies are not accepted, no behavior tracking will take place.”

“Secondly, a number of privacy safeguards have been put in place,” David Martens continues. “Users are automatically ‘forgotten’ after 30 days, their online behavior is only tracked on premium web pages – not across the whole of the Internet – and their data is never sold to other parties.”

PrediCube: bringing together the best in research and entrepreneurship

PrediCube builds on the outcomes of the DiDaM project, a collaborative research effort under the banner of iMinds Media. DiDaM investigated ways of analyzing media users’ Internet sessions in real-time and identifying patterns to help advertisers integrate relevant ads into web pages (also in real-time). Objective: providing consumers with just those ads that are relevant to them. DiDaM’s research findings and the expertise from the Applied Data Mining research center of the University of Antwerp together laid the foundation of PrediCube. The PrediCube team can also count on the support of the Start it @KBC incubator – providing them with business guidance and office space.

 

About PrediCube

PrediCube is a new tech startup that uses advanced big data technology to predict which online users are interested in a product, allowing targeted advertising on premium websites. Or how a spinoff company of University of Antwerp and iMinds is ready to go head to head with Facebook and Google to compete for online advertising budgets. PrediCube results from an iMinds Media project that ended in 2014, investigating the potential of data for improved advertising (together with partners Concentra, Pebble Media, AdHese and KU Leuven). PrediCube has been co-founded by prof. David Martens, who heads the Applied Data Mining research group at the University of Antwerp (faculty of Applied Economic Sciences), and whose research focuses on the development and application of data mining algorithms. PrediCube was founded in October 2014, and is part of the Start it @KBC incubator. Current customers include Batibouw, Engels Ramen, Verandaland and Triple Living.

Contact

Press coverage about this lauch:

Nice article in Dutch about our last Meetup we did at the VUBrussels – Datascience in the Telecom world

technologiumabout telecom

 

Antoine from Technologium wrote this nice article about our last meetup we held at the VUB focusing on the Telecom world  and their use of datasciences.

technologium  Alles wat je moet weten over Big Data in de Telecom

 

 

See you soon  at our next event.

DIS2015

 

Job – Big Industries – Hadoop Developer

Big-Industries-stamp-logo

Matthias Vallaey      Matthias Vallaey, Partner at Big Industries asked us to post following vacancy

Big Industries (a Cronos Company) works together with you to translate your ideas into workable Big Data solutions that will create measurable value for your organisation.
Implementing the solution using proven big data technologies from industry leading vendors, integrating only the most appropriate, effective and sustainable technologies to deliver best-in-class products and services.
Big Industries helps to assess, identify and integrate effective refinements in order to increase the value that big data solutions bring.
We are fulfillment partners for Cloudera and MapR, the premiere Hadoop distributions, for BeLux and offer expert consulting, systems integration and tailored application development with knowledge and experience across a broad range of industries.

Specialties

Hadoop, Big Data, Systems Integration, Consulting, HBase, Spark, MapReduce, SolrCloud, Impala, Kafka

Job Description

As a Big Data Developer you will work in a team building big data solutions. You will be developing, maintaining, testing and evaluating big data solutions within organisations. Generally you will be working on implementing complex and large scale big data projects with a focus on collecting, parsing, managing, analysing and visualizing large datasets to turn raw data into insights using multiple toolsets, techniques and platforms.

Soft skills

Team player – embraces change, able to adapt to working in varied software delivery environments. Can-do attitude, pragmatic, results-oriented – lateral thinker.

Mandatory experience & skills

  • Computing or Mathematics diploma, or 4 years experience active work experience within systems integration teams.
  • Thorough understanding of Java, and solid grasp of software development best practises.
  • Experience using hadoop and related technologies (eg. pig, hive, spark, impala), ideally with popular hadoop data processing pipeline patterns and technologies (cascading, crunch, oozie).
  • Willing to work to become Cloudera Developer certified.
  • Development exposure on both cloud and classic compute environments.
  • Very good Linux systems and Linux shell scripting knowledge.

Apply:

Make sure that you are a member of the Brussels Data Science Community linkedin group before you apply. Join  here.

Here is the original jobpost .

Contact Matthias Vallaey matthias.vallaey@bigindustries.be (+32 496 57 66 27).

Job – Mediahuis – Team Manager Big Data

mediahuis

Sarah Vandervreken
We kregen van Sarah Vandervreken, HR Recruitment & Selection Officer at Mediahuis België het volgend vriendelijk verzoek:

February 20, 2015 2:37 PM

Dag Philippe,

Ik zag dat jij trekker bent van Brussels Data Science Community.
We hebben bij Mediahuis momenteel een toffe verantwoordelijke job openstaan binnen ons groeiend 360° datateam http://mediahuis.be/job/team-manager-big-data.
Kan ik deze posten in de community of daar wat extra visibiliteit rond geven?

Alvast bedankt!

Groetjes,
Sarah

03/210 03 73
sarah.vandervreken@gmail.com

Functiebeschrijving

  • Je bent verantwoordelijk voor het begeleiden van een aantal technische projecten rond klantenidentificatie, geautomatiseerde gerichte campagnes, omni-channel campagnes en profileren van klantengedrag (big data).
  • Je werkt in nauw overleg met de collega’s van sales en marketing over de doelstellingen, de aanpak van de campagnes, timings van de op te leveren opdrachten en projecten van je team.
  • Je zorgt er ook voor dat data/meten een essentieel operationeel middel wordt bij zowel redacties, marketing, sales als de digitale afdeling bij het nemen van tactische en strategische beslissingen en je zet hiertoe de nodige structuren en contacten op om dit te faciliteren in de komende jaren.
  • Je geeft leiding aan een klein team van ervaren technische specialisten. Je coacht hen, overlegt regelmatig, stelt werkplanningen op, en je evalueert en stuurt bij waar nodig.
  • Je bent een technisch klankbord bij wie je teamleden terecht kunnen in geval van vragen.

Profiel

  • Je beschikt over een hogere opleiding in ICT/handelswetenschappen of een gelijkaardige richting
  • Je hebt minstens enkele jaren technische project management ervaring en een agile mindset.
  • Ervaring met 1 of meerdere van de volgende technologieën is gewenst:
    • .NET, webservices, data-access
    • Java, bestaande code kunnen begrijpen en aanpassen
    • javascript/html
    • Google Analytics
    • Selligent
  • People management ervaring vormt een plus.
  • Je hebt een passie voor marketing automation en/of big data en interesse in de veranderende mediasector.
  • Je herkent je in de volgende competenties:
    • Je werkt planmatig en bent sterk in het opvolgen en bijsturen van mensen en projecten.
    • Je werkt constructief met anderen samen, adviseert hen en reikt doordachte oplossingen aan.
    • Je functioneert effectief onder druk in dit dynamisch team.
    • Je neemt verantwoordelijkheid op en toont initiatief. Je bent gedreven.

Wij bieden

  • Een aantrekkelijk salaris en een pakket extralegale voordelen overeenstemmend met je ervaring en competenties.
  • Je plaats van tewerkstelling is Antwerpen, met occasionele verplaatsingen naar onze vestigingen te Groot-Bijgaarden en Hasselt.
  • Een boeiende job met ruimte voor eigen initiatieven en verantwoordelijkheden bij een gevestigd en snelgroeiend mediabedrijf. Je bent verantwoordelijk voor een dynamisch en enthousiast team, met ervaren mensen op de diverse terreinen. Je bent nauw betrokken met de business waardoor je snel resultaten ziet.

Apply:

Make sure that you are a member of the Brussels Data Science Community linkedin group before you apply. Join  here.

Here is the original jobpost .

If you have questions please contact Sarah Vandervreken  03/210 03 73 or 02/467 25 08 (on Wednesday).

Apply here: SOLLICITEER

Job – NG DATA – Big Data Scientist

NGDATA

Job Description

In the era of Big Data, data is not useful until we identify patterns, apply context and intelligence. The data scientist, as an emerging career path, is at the core of organizational success with Big Data and for humanizing the data to help businesses better understands their consumer.

As a data scientist, you sift through the explosion of data to discover what the data is telling you. You figure out “what questions to ask” so that relevant information hidden in the large volumes and varieties of data can be extracted. The Data Scientist will be responsible for designing and implementing processes and layouts for complex, large-scale data sets used for modeling, data mining, and research purposes.

Opportunities

  • Be a true partner in defining the solutions, have and develop business acumen and bring technical perspective in furthering the product and business;
  • Aggregate data from various sources;
  • Help define, design, and build projects that leverage our data;
  • Develop computational algorithms and statistical methods that find patterns and relationships in large volumes of data;
  • Determine and implement mechanisms to improve our data quality;
  • Deliver clear, well-communicated and complete design documents;
  • Ability to work in a team as well as independently and deliver on aggressive goals;
  • Exhibit Creativity and resourcefulness at problem solving while collaborating and working effectively with best in class designers, engineers of different technical backgrounds, architects and product managers.

Personal Skills

  • You have a logical approach to the solution of problems and good conceptual ability and skills in analysis;
  • You have the ability to integrate research and best practices into problem avoidance and continuous improvement
  • You possess good interpersonal skills;
  • You are self reliant and capable of both independent work and as member of a team;
  • You are persistent, accurate, imaginative;
  • You are able and have the discipline to document and record results;
  • Be customer service oriented;
  • Be open minded and solution oriented;
  • You enjoy constantly expanding your knowledge base;
  • You are willing to travel up to five days per month.

Technical Background

The successful candidate should have 5+ years experience in large-scale software development, with at least 3 years in Hadoop. Have a strong cross-functional technical background, excellent written/oral communication skills, and a willingness and capacity to expand their leadership and technical skills.

  • BS / MS in computer Science;
  • Strong understanding of data mining and machine learning algorithms, data structures and related core software engineering concepts;
  • Understanding the concepts of Hadoop, HBase and other big data technologies; Understanding of marketing processes in the financial and or retail market;
  • Have a sound knowledge of SPSS and SQL

Apply:

Make sure that you are a member of the Brussels Data Science Community linkedin group before you apply. Join  here.

Here is the original jobpost .

Apply:  Upload your resume or send it to jobs@ngdata.com. We look forward to your application!